As the computing world moves from the information age into the knowledge-based age, it is beneficial to induce knowledge from the information superhighway formed from the Internet and intranet. The knowledge acquired can be expressed in different knowledge representations such as computer programs, first-order logical relations, or fuzzy Petri nets (FPNs). In this paper, we present a flexible knowledge discovery system called generic genetic programming (GGP) that applies genetic programming (GP) and logic grammars to learn knowledge in various knowledge representation formalisms. An experiment is performed to demonstrate that GGP can discover knowledge represented in FPNs that support fuzzy and approximate reasoning. To evaluate the performance of GGP in producing good FPNs, the classification accuracy of the FPN induced by GGP and that of the decision tree generated by C4.5 are compared. Moreover, the performance of GGP in inducing logic programs from noisy examples is evaluated. A detailed comparison to FOIL, a system that induces logic programs, has been conducted. These experiments demonstrate that GGP is a promising alternative to other knowledge discovery systems and sometimes is superior for handling noisy and inexact data.